


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		DebertaTokenizer
				SCREAMING_SNAKE_CASE__				:			Union[str, Any]						=		True
				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		DebertaTokenizerFast


				def 		__magic_name__(       self			:List[str]	)		->			str:
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''[UNK]''',
										]
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		dict(zip(lowerCAmelCase__			,			range(len(lowerCAmelCase__	)	)	)	)
										__SCREAMING_SNAKE_CASE						:     int					=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{'''unk_token''': '''[UNK]'''}

										__SCREAMING_SNAKE_CASE						:     str					=		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file''']	)
										with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8'''	) as fp:
																fp.write(json.dumps(lowerCAmelCase__	) + '''\n'''	)
										with open(self.merges_file			,			'''w'''			,			encoding='''utf-8'''	) as fp:
																fp.write('''\n'''.join(lowerCAmelCase__	)	)


				def 		__magic_name__(       self			:Any			,			**lowerCAmelCase__			:str	)		->			Union[str, Any]:
										kwargs.update(self.special_tokens_map	)
										return self.tokenizer_class.from_pretrained(self.tmpdirname			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:int	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		'''lower newer'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''lower newer'''
										return input_text, output_text


				def 		__magic_name__(       self			:str	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.get_tokenizer()
										__SCREAMING_SNAKE_CASE						:     Tuple					=		'''lower newer'''
										__SCREAMING_SNAKE_CASE						:     str					=		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		tokenizer.tokenize(lowerCAmelCase__	)
										self.assertListEqual(lowerCAmelCase__			,			lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokens + [tokenizer.unk_token]
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)			,			lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.get_tokenizer()
										__SCREAMING_SNAKE_CASE						:     List[str]					=		tokenizer('''Hello'''			,			'''World'''	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']			,			lowerCAmelCase__	)


				@slow
				def 		__magic_name__(       self			:Optional[int]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''	)

										__SCREAMING_SNAKE_CASE						:     int					=		tokenizer.encode('''sequence builders'''			,			add_special_tokens=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     str					=		tokenizer.encode('''multi-sequence build'''			,			add_special_tokens=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer.encode(
										    '''sequence builders'''			,			add_special_tokens=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer.encode(
										    '''sequence builders'''			,			'''multi-sequence build'''			,			add_special_tokens=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Any					=		tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     str					=		tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__			,			lowerCAmelCase__	)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode



				@slow
				def 		__magic_name__(       self			:Any	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		[self.tokenizer_class]
										if self.test_rust_tokenizer:
																tokenizer_classes.append(self.rust_tokenizer_class	)

										for tokenizer_class in tokenizer_classes:
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		tokenizer_class.from_pretrained('''microsoft/deberta-base'''	)

																__SCREAMING_SNAKE_CASE						:     Dict					=		[
																    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																    '''ALBERT incorporates two parameter reduction techniques''',
																    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																    ''' vocabulary embedding.''',
																]

																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer(lowerCAmelCase__			,			padding=lowerCAmelCase__	)
																__SCREAMING_SNAKE_CASE						:     Tuple					=		[tokenizer.decode(lowerCAmelCase__			,			skip_special_tokens=lowerCAmelCase__	) for seq in encoding['''input_ids''']]

																# fmt: off
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		{
																    '''input_ids''': [
																        [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 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, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
																    ],
																    '''token_type_ids''': [
																        [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, 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, 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],
																        [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]
																    ]
																}
																# fmt: on

																__SCREAMING_SNAKE_CASE						:     List[str]					=		[
																    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																    '''ALBERT incorporates two parameter reduction techniques''',
																    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																    ''' vocabulary embedding.''',
																]

																self.assertDictEqual(encoding.data			,			lowerCAmelCase__	)

																for expected, decoded in zip(lowerCAmelCase__			,			lowerCAmelCase__	):
																						self.assertEqual(lowerCAmelCase__			,			lowerCAmelCase__	)







'''simple docstring'''
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Tuple				,  __lowercase						:  Tuple     ) ->      Tuple:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  List[str]="attention"     ) ->      Optional[Any]:



       """simple docstring"""






       __UpperCamelCase       =  __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  k_tmp.reshape(k_tmp.shape[0]				,  k_tmp.shape[1] * k_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,  o_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  q_tmp.reshape(q_tmp.shape[0]				,  q_tmp.shape[1] * q_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  v_tmp.reshape(v_tmp.shape[0]				,  v_tmp.shape[1] * v_tmp.shape[2]     )
       return k, o, q, v
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict				,  __lowercase						:  int				,  __lowercase						:  List[Any]=False     ) ->      Optional[Any]:



       """simple docstring"""






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

       __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
       return wi, wo
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[Any]				,  __lowercase						:  List[str]				,  __lowercase						:  Optional[int]     ) ->      str:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def 				lowercase__   (   __lowercase						:  dict				,  *, __lowercase						:  int				,  __lowercase						:  bool				,  __lowercase						:  bool = False     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  traverse_util.flatten_dict(variables['target']     )
       __UpperCamelCase       =  {'/'.join(__lowercase     ): v for k, v in old.items()}

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

       __UpperCamelCase       =  collections.OrderedDict()

       # Shared embeddings.
       __UpperCamelCase       =  old['token_embedder/embedding']

       # Encoder.
       for i in range(__lowercase     ):
              # Block i, layer 0 (Self Attention).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_attention_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'attention'     )
              __UpperCamelCase       =  layer_norm
              __UpperCamelCase       =  k.T
              __UpperCamelCase       =  o.T
              __UpperCamelCase       =  q.T
              __UpperCamelCase       =  v.T

              # Block i, layer 1 (MLP).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_mlp_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'encoder'				,  __lowercase     )
              __UpperCamelCase       =  layer_norm
              if split_mlp_wi:
                     __UpperCamelCase       =  wi[0].T
                     __UpperCamelCase       =  wi[1].T
              else:
                     __UpperCamelCase       =  wi.T
              __UpperCamelCase       =  wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     __UpperCamelCase       =  tax_relpos_bias_lookup(
                         __lowercase				,  __lowercase				,  'encoder'     ).T

       __UpperCamelCase       =  old['encoder/encoder_norm/scale']

       if not scalable_attention:
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'encoder'     ).T
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'decoder'     ).T

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

                     # Block i, layer 1 (Cross Attention).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_cross_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'encoder_decoder_attention'     )
                     __UpperCamelCase       =  layer_norm
                     __UpperCamelCase       =  k.T
                     __UpperCamelCase       =  o.T
                     __UpperCamelCase       =  q.T
                     __UpperCamelCase       =  v.T

                     # Block i, layer 2 (MLP).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_mlp_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'decoder'				,  __lowercase     )
                     __UpperCamelCase       =  layer_norm
                     if split_mlp_wi:
                            __UpperCamelCase       =  wi[0].T
                            __UpperCamelCase       =  wi[1].T
                     else:
                            __UpperCamelCase       =  wi.T
                     __UpperCamelCase       =  wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            __UpperCamelCase       =  tax_relpos_bias_lookup(__lowercase				,  __lowercase				,  'decoder'     ).T

              __UpperCamelCase       =  old['decoder/decoder_norm/scale']

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     __UpperCamelCase       =  old['decoder/logits_dense/kernel'].T

       return new
def 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  bool     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  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:
              __UpperCamelCase       =  state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     __UpperCamelCase       =  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.'     )
                     __UpperCamelCase       =  state_dict['shared.weight']

       return state_dict
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Dict				,  __lowercase						:  str				,  __lowercase						:  int				,  __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  checkpoints.load_tax_checkpoint(__lowercase     )
       __UpperCamelCase       =  convert_tax_to_pytorch(
           __lowercase				,  num_layers=config.num_layers				,  is_encoder_only=__lowercase				,  scalable_attention=__lowercase     )
       __UpperCamelCase       =  make_state_dict(__lowercase				,  __lowercase     )
       model.load_state_dict(__lowercase				,  strict=__lowercase     )
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  bool = False				,  __lowercase						:  bool = False				,  ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  MTaConfig.from_json_file(__lowercase     )
       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:
              __UpperCamelCase       =  UMTaEncoderModel(__lowercase     )
       else:
              __UpperCamelCase       =  UMTaForConditionalGeneration(__lowercase     )

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

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

       # Verify that we can load the checkpoint.
       model.from_pretrained(__lowercase     )
       print('Done'     )


if __name__ == "__main__":
    a__							:							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
    )
    parser.add_argument(
        '''--scalable_attention''',
        action='''store_true''',
        help='''Whether the model uses scaled attention (umt5 model)''',
        default=False,
    )
    a__							:							List[str] =parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )
