import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class    snake_case       (   UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  PhobertTokenizer
  lowercase_								=  False




  def 						__lowercase(   self  :						int					)->							Optional[int]:




       """simple docstring"""
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       SCREAMING_SNAKE_CASE__					:					Tuple      =		['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
       SCREAMING_SNAKE_CASE__					:					str      =		dict(zip(a_  ,			range(len(a_					)					)					)					)
       SCREAMING_SNAKE_CASE__					:					Any      =		['#version: 0.2', 'l à</w>']
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		{'unk_token': '<unk>'}

       SCREAMING_SNAKE_CASE__					:					Any      =		os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES['vocab_file']					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES['merges_file']					)

       with open(self.vocab_file  ,			'w'  ,			encoding='utf-8'					) as fp:
            for token in vocab_tokens:
                 fp.write(F'''{token} {vocab_tokens[token]}\n'''					)
       with open(self.merges_file  ,			'w'  ,			encoding='utf-8'					) as fp:
            fp.write('\n'.join(a_					)					)




  def 						__lowercase(   self  :						Any  ,			**a_  :						Any					)->							str:




       """simple docstring"""
       kwargs.update(self.special_tokens_map					)
       return PhobertTokenizer.from_pretrained(self.tmpdirname  ,			**a_					)




  def 						__lowercase(   self  :						Any  ,			a_  :						Optional[Any]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		'Tôi là VinAI Research'
       SCREAMING_SNAKE_CASE__					:					List[str]      =		'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
       return input_text, output_text




  def 						__lowercase(   self  :						Tuple					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		PhobertTokenizer(self.vocab_file  ,			self.merges_file  ,			**self.special_tokens_map					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		'Tôi là VinAI Research'
       SCREAMING_SNAKE_CASE__					:					Tuple      =		'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		tokenizer.tokenize(a_					)
       print(a_					)
       self.assertListEqual(a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		tokens + [tokenizer.unk_token]

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a_					)  ,			a_					)




"""simple docstring"""






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Optional[int]="attention"						)					->       str:
      '''simple docstring'''


      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
      return k, o, q, v


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Union[str, Any]=False						)					->       str:
      '''simple docstring'''


      if split_mlp_wi:
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
            lowercase								=      (wi_a, wi_a)
      else:
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi/kernel']

      lowercase								=      params[f'{prefix}/layers_{i}/mlp/wo/kernel']
      return wi, wo


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:int						)					->       Any:
      '''simple docstring'''


      return params[f'{prefix}/layers_{i}/{layer_name}/scale']


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						*, lowerCAmelCase__	:int   ,						lowerCAmelCase__	:bool						)					->       List[str]:
      '''simple docstring'''


      lowercase								=      traverse_util.flatten_dict(variables["""target"""]						)
      lowercase								=      {"""/""".join(lowerCAmelCase__						): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      lowercase								=      """encoder/layers_0/mlp/wi_0/kernel""" in old
      print("""Split MLP:"""   ,						lowerCAmelCase__						)

      lowercase								=      collections.OrderedDict()

      # Shared embeddings.
      lowercase								=      old["""token_embedder/embedding"""]

      # Encoder.
      for i in range(lowerCAmelCase__						):
            # Block i, layer 0 (Self Attention).
            lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""pre_attention_layer_norm"""						)
            lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""attention"""						)
            lowercase								=      layer_norm
            lowercase								=      k.T
            lowercase								=      o.T
            lowercase								=      q.T
            lowercase								=      v.T

            # Block i, layer 1 (MLP).
            lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""pre_mlp_layer_norm"""						)
            lowercase				,				lowercase								=      tax_mlp_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						lowerCAmelCase__						)
            lowercase								=      layer_norm
            if split_mlp_wi:
                  lowercase								=      wi[0].T
                  lowercase								=      wi[1].T
            else:
                  lowercase								=      wi.T
            lowercase								=      wo.T

      lowercase								=      old[
          """encoder/relpos_bias/rel_embedding"""
      ].T
      lowercase								=      old["""encoder/encoder_norm/scale"""]

      if not is_encoder_only:
            # Decoder.
            for i in range(lowerCAmelCase__						):
                  # Block i, layer 0 (Self Attention).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_self_attention_layer_norm"""						)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""self_attention"""						)
                  lowercase								=      layer_norm
                  lowercase								=      k.T
                  lowercase								=      o.T
                  lowercase								=      q.T
                  lowercase								=      v.T

                  # Block i, layer 1 (Cross Attention).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_cross_attention_layer_norm"""						)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""encoder_decoder_attention"""						)
                  lowercase								=      layer_norm
                  lowercase								=      k.T
                  lowercase								=      o.T
                  lowercase								=      q.T
                  lowercase								=      v.T

                  # Block i, layer 2 (MLP).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_mlp_layer_norm"""						)
                  lowercase				,				lowercase								=      tax_mlp_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						lowerCAmelCase__						)
                  lowercase								=      layer_norm
                  if split_mlp_wi:
                        lowercase								=      wi[0].T
                        lowercase								=      wi[1].T
                  else:
                        lowercase								=      wi.T
                  lowercase								=      wo.T

            lowercase								=      old["""decoder/decoder_norm/scale"""]
            lowercase								=      old[
                """decoder/relpos_bias/rel_embedding"""
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  lowercase								=      old["""decoder/logits_dense/kernel"""].T

      return new


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:bool						)					->       int:
      '''simple docstring'''


      lowercase								=      collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            lowercase								=      state_dict["""shared.weight"""]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  lowercase								=      state_dict["""shared.weight"""]

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print("""Using shared word embeddings as lm_head."""						)
                  lowercase								=      state_dict["""shared.weight"""]

      return state_dict


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:List[Any]						)					->       Union[str, Any]:
      '''simple docstring'''


      lowercase								=      checkpoints.load_tax_checkpoint(lowerCAmelCase__						)
      lowercase								=      convert_tax_to_pytorch(lowerCAmelCase__   ,						num_layers=config.num_layers   ,						is_encoder_only=lowerCAmelCase__						)
      lowercase								=      make_state_dict(lowerCAmelCase__   ,						lowerCAmelCase__						)
      model.load_state_dict(lowerCAmelCase__   ,						strict=lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:bool = False						)					->       Tuple:
      '''simple docstring'''


      lowercase								=      TaConfig.from_json_file(lowerCAmelCase__						)
      print(f'Building PyTorch model from configuration: {config}'						)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            lowercase								=      TaEncoderModel(lowerCAmelCase__						)
      else:
            lowercase								=      TaForConditionalGeneration(lowerCAmelCase__						)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      model.save_pretrained(lowerCAmelCase__						)

      # Verify that we can load the checkpoint.
      model.from_pretrained(lowerCAmelCase__						)
      print("""Done"""						)


if __name__ == "__main__":
       __lowerCAmelCase       :			List[Any]   	=argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
       )
       __lowerCAmelCase       :			List[Any]   	=parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )



