




'''simple docstring'''





import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						GPTSanJapaneseTokenizer
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						False
     SCREAMING_SNAKE_CASE_							:   int       =						{"""do_clean_text""": False, """add_prefix_space""": False}






     def     _lowercase     (			self   )							->      Union[str, Any]:
          super().setUp()

          # fmt: off
          _snake_case							=				["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
          # fmt: on
          _snake_case							=				{"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
          _snake_case							=				{"unk_token": "<unk>"}

          _snake_case							=				os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"]   )
          _snake_case							=				os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["emoji_file"]   )
          with open(self.vocab_file ,"w" ,encoding="utf-8"   ) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )
          with open(self.emoji_file ,"w"   ) as emoji_writer:
               emoji_writer.write(json.dumps(_SCREAMING_SNAKE_CASE   )   )






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      Tuple:
          kwargs.update(self.special_tokens_map   )
          return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          _snake_case							=				"こんにちは、世界。 \nこんばんは、㔺界。😀"
          _snake_case							=				"こんにちは、世界。 \nこんばんは、世界。😀"
          return input_text, output_text






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							, _snake_case							=				self.get_input_output_texts(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer.encode(_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer.decode(_SCREAMING_SNAKE_CASE ,clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE   )
          return text, ids






     def     _lowercase     (			self   )							->      Union[str, Any]:
          pass  # TODO add if relevant






     def     _lowercase     (			self   )							->      str:
          pass  # TODO add if relevant






     def     _lowercase     (			self   )							->      Optional[Any]:
          pass  # TODO add if relevant






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.get_tokenizer()

          # Testing tokenization
          _snake_case							=				"こんにちは、世界。　こんばんは、㔺界。"
          _snake_case							=				["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
          _snake_case							=				tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          # Testing conversion to ids without special tokens
          _snake_case							=				[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
          _snake_case							=				tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          # Testing conversion to ids with special tokens
          _snake_case							=				tokens + [tokenizer.unk_token]
          _snake_case							=				[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
          _snake_case							=				tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.get_tokenizer()

          # Testing tokenization
          _snake_case							=				"こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
          _snake_case							=				"こんにちは、、、、世界。こんばんは、、、、世界。"
          _snake_case							=				tokenizer.encode(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer.decode(_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"   )

          # Testing tokenization
          _snake_case							=				"こんにちは、世界。"
          _snake_case							=				"こんばんは、㔺界。😀"
          _snake_case							=				"こんにちは、世界。こんばんは、世界。😀"
          _snake_case							=				tokenizer.encode(prefix_text + input_text   )
          _snake_case							=				tokenizer.encode("" ,prefix_text=prefix_text + input_text   )
          _snake_case							=				tokenizer.encode(_SCREAMING_SNAKE_CASE ,prefix_text=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer.decode(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer.decode(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer.decode(_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"   )

          # Testing tokenization
          _snake_case							=				"こんにちは、世界。"
          _snake_case							=				"こんばんは、㔺界。😀"

          _snake_case							=				len(tokenizer.encode(_SCREAMING_SNAKE_CASE   )   ) - 2
          _snake_case							=				len(tokenizer.encode(_SCREAMING_SNAKE_CASE   )   ) - 2

          _snake_case							=				[1] + [0] * (len_prefix + len_text + 1)
          _snake_case							=				[1] * (len_prefix + len_text + 1) + [0]
          _snake_case							=				[1] + [1] * (len_prefix) + [0] * (len_text + 1)

          _snake_case							=				tokenizer(prefix_text + input_text   ).token_type_ids
          _snake_case							=				tokenizer("" ,prefix_text=prefix_text + input_text   ).token_type_ids
          _snake_case							=				tokenizer(_SCREAMING_SNAKE_CASE ,prefix_text=_SCREAMING_SNAKE_CASE   ).token_type_ids
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"   )

          _snake_case							=				tokenizer.encode("あンいワ"   )
          _snake_case							=				tokenizer.encode("" ,prefix_text="あンいワ"   )
          _snake_case							=				tokenizer.encode("いワ" ,prefix_text="あン"   )

          self.assertEqual(tokenizer.decode(_SCREAMING_SNAKE_CASE   ) ,tokenizer.decode(_SCREAMING_SNAKE_CASE   )   )
          self.assertEqual(tokenizer.decode(_SCREAMING_SNAKE_CASE   ) ,tokenizer.decode(_SCREAMING_SNAKE_CASE   )   )
          self.assertNotEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertNotEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(x_token_a[1] ,x_token_a[-1]   )  # SEG token
          self.assertEqual(x_token_a[1] ,x_token_a[3]   )  # SEG token






     @slow
     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"   )

          _snake_case							=				[["武田信玄", "は、"], ["織田信長", "の配下の、"]]
          _snake_case							=				tokenizer(_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer.batch_encode_plus(_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE   )

          # fmt: off
          _snake_case							=				[[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
          _snake_case							=				[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
          _snake_case							=				[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
          # fmt: on
          self.assertListEqual(x_token.input_ids ,_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(x_token.token_type_ids ,_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(x_token.attention_mask ,_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(x_token_a.input_ids ,_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(x_token_a.token_type_ids ,_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(x_token_a.attention_mask ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          # Intentionally convert some words to accommodate character fluctuations unique to Japanese
          pass






     def     _lowercase     (			self   )							->      Optional[int]:
          # tokenizer has no padding token
          pass








'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def      __a     (       _UpperCamelCase:   Tuple   )						->			Union[str, Any]:

     """simple docstring"""




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


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


