






'''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    ):





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


if __name__ == "__main__":
			__lowerCamelCase						:    Dict     				=  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""")
			__lowerCamelCase						:    Optional[int]     				=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)








import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__  							=	'''▁'''

__magic_name__  							=	get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		List[str]     						=  BertGenerationTokenizer
      _A    :		int     						=  False
      _A    :		Optional[Any]     						=  True
      def A_      (      self			):
          super().setUp()

          snake_case__    =					BertGenerationTokenizer(lowerCamelCase		,						keep_accents=lowerCamelCase			)
          tokenizer.save_pretrained(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					"<s>"
          snake_case__    =					1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase			)		,						lowerCamelCase			)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase			)		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					list(self.get_tokenizer().get_vocab().keys()			)

          self.assertEqual(vocab_keys[0]		,						"<unk>"			)
          self.assertEqual(vocab_keys[1]		,						"<s>"			)
          self.assertEqual(vocab_keys[-1]		,						"<pad>"			)
          self.assertEqual(len(lowerCamelCase			)		,						10_02			)
      def A_      (      self			):
          self.assertEqual(self.get_tokenizer().vocab_size		,						10_00			)
      def A_      (      self			):
          snake_case__    =					BertGenerationTokenizer(lowerCamelCase		,						keep_accents=lowerCamelCase			)

          snake_case__    =					tokenizer.tokenize("This is a test"			)
          self.assertListEqual(lowerCamelCase		,						["▁This", "▁is", "▁a", "▁t", "est"]			)

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(lowerCamelCase			)		,						[2_85, 46, 10, 1_70, 3_82]		,						)

          snake_case__    =					tokenizer.tokenize("I was born in 92000, and this is falsé."			)
          self.assertListEqual(
              lowerCamelCase		,						[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "9",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "é",
                  ".",
              ]		,						)
          snake_case__    =					tokenizer.convert_tokens_to_ids(lowerCamelCase			)
          self.assertListEqual(
              lowerCamelCase		,						[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,						)

          snake_case__    =					tokenizer.convert_ids_to_tokens(lowerCamelCase			)
          self.assertListEqual(
              lowerCamelCase		,						[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "<unk>",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "<unk>",
                  ".",
              ]		,						)
      @cached_property
      def A_      (      self			):
          return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"			)
      @slow
      def A_      (      self			):
          snake_case__    =					"Hello World!"
          snake_case__    =					[1_85_36, 22_60, 1_01]

          self.assertListEqual(lowerCamelCase		,						self.big_tokenizer.encode(lowerCamelCase			)			)
      @slow
      def A_      (      self			):
          snake_case__    =					(
              "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
              " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
          )
          snake_case__    =					[
              8_71,
              4_19,
              3_58,
              9_46,
              9_91,
              25_21,
              4_52,
              3_58,
              13_57,
              3_87,
              77_51,
              35_36,
              1_12,
              9_85,
              4_56,
              1_26,
              8_65,
              9_38,
              54_00,
              57_34,
              4_58,
              13_68,
              4_67,
              7_86,
              24_62,
              52_46,
              11_59,
              6_33,
              8_65,
              45_19,
              4_57,
              5_82,
              8_52,
              25_57,
              4_27,
              9_16,
              5_08,
              4_05,
              3_43_24,
              4_97,
              3_91,
              4_08,
              1_13_42,
              12_44,
              3_85,
              1_00,
              9_38,
              9_85,
              4_56,
              5_74,
              3_62,
              1_25_97,
              32_00,
              31_29,
              11_72,
          ]

          self.assertListEqual(lowerCamelCase		,						self.big_tokenizer.encode(lowerCamelCase			)			)
      @require_torch
      @slow
      def A_      (      self			):
          import torch

          from transformers import BertGenerationConfig, BertGenerationEncoder

          # Build sequence
          snake_case__    =					list(self.big_tokenizer.get_vocab().keys()			)[:10]
          snake_case__    =					" ".join(lowerCamelCase			)
          snake_case__    =					self.big_tokenizer.encode_plus(lowerCamelCase		,						return_tensors="pt"		,						return_token_type_ids=lowerCamelCase			)
          snake_case__    =					self.big_tokenizer.batch_encode_plus(
              [sequence + " " + sequence]		,						return_tensors="pt"		,						return_token_type_ids=lowerCamelCase			)

          snake_case__    =					BertGenerationConfig()
          snake_case__    =					BertGenerationEncoder(lowerCamelCase			)

          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
              model(**lowerCamelCase			)
              model(**lowerCamelCase			)



      @slow
      def A_      (      self			):
          # fmt: off
          snake_case__    =					{"input_ids": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=lowerCamelCase		,						model_name="google/bert_for_seq_generation_L-24_bbc_encoder"		,						revision="c817d1fd1be2ffa69431227a1fe320544943d4db"		,						)



