





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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase="attention" )			->     Dict:
    lowerCamelCase__    :      int					 =		params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
    lowerCamelCase__    :      int					 =		params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
    lowerCamelCase__    :      List[Any]					 =		params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
    lowerCamelCase__    :      str					 =		params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
    return k, o, q, v


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False )			->     List[Any]:
    if split_mlp_wi:
        lowerCamelCase__    :      Optional[Any]					 =		params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
        lowerCamelCase__    :      Optional[int]					 =		params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
        lowerCamelCase__    :      Union[str, Any]					 =		(wi_a, wi_a)
    else:
        lowerCamelCase__    :      Tuple					 =		params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

    lowerCamelCase__    :      str					 =		params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
    return wi, wo


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Any:
    return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	*, _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    lowerCamelCase__    :      List[str]					 =		traverse_util.flatten_dict(variables['target'] )
    lowerCamelCase__    :      Optional[int]					 =		{'/'.join(_UpperCAmelCase ): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    lowerCamelCase__    :      Optional[Any]					 =		'encoder/layers_0/mlp/wi_0/kernel' in old
    print('Split MLP:'						,	_UpperCAmelCase )

    lowerCamelCase__    :      Optional[Any]					 =		collections.OrderedDict()

    # Shared embeddings.
    lowerCamelCase__    :      Optional[Any]					 =		old['token_embedder/embedding']

    # Encoder.
    for i in range(_UpperCAmelCase ):
        # Block i, layer 0 (Self Attention).
        lowerCamelCase__    :      str					 =		tax_layer_norm_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'encoder'						,	'pre_attention_layer_norm' )
        lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		tax_attention_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'encoder'						,	'attention' )
        lowerCamelCase__    :      Dict					 =		layer_norm
        lowerCamelCase__    :      List[Any]					 =		k.T
        lowerCamelCase__    :      Dict					 =		o.T
        lowerCamelCase__    :      Optional[int]					 =		q.T
        lowerCamelCase__    :      Union[str, Any]					 =		v.T

        # Block i, layer 1 (MLP).
        lowerCamelCase__    :      Dict					 =		tax_layer_norm_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'encoder'						,	'pre_mlp_layer_norm' )
        lowerCamelCase__ ,							lowerCamelCase__    :      Any					 =		tax_mlp_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'encoder'						,	_UpperCAmelCase )
        lowerCamelCase__    :      List[Any]					 =		layer_norm
        if split_mlp_wi:
            lowerCamelCase__    :      str					 =		wi[0].T
            lowerCamelCase__    :      Dict					 =		wi[1].T
        else:
            lowerCamelCase__    :      Optional[Any]					 =		wi.T
        lowerCamelCase__    :      Tuple					 =		wo.T

    lowerCamelCase__    :      int					 =		old[
        'encoder/relpos_bias/rel_embedding'
    ].T
    lowerCamelCase__    :      int					 =		old['encoder/encoder_norm/scale']

    if not is_encoder_only:
        # Decoder.
        for i in range(_UpperCAmelCase ):
            # Block i, layer 0 (Self Attention).
            lowerCamelCase__    :      Optional[int]					 =		tax_layer_norm_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'decoder'						,	'pre_self_attention_layer_norm' )
            lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		tax_attention_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'decoder'						,	'self_attention' )
            lowerCamelCase__    :      List[Any]					 =		layer_norm
            lowerCamelCase__    :      List[Any]					 =		k.T
            lowerCamelCase__    :      Union[str, Any]					 =		o.T
            lowerCamelCase__    :      str					 =		q.T
            lowerCamelCase__    :      int					 =		v.T

            # Block i, layer 1 (Cross Attention).
            lowerCamelCase__    :      str					 =		tax_layer_norm_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'decoder'						,	'pre_cross_attention_layer_norm' )
            lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		tax_attention_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'decoder'						,	'encoder_decoder_attention' )
            lowerCamelCase__    :      str					 =		layer_norm
            lowerCamelCase__    :      List[str]					 =		k.T
            lowerCamelCase__    :      Tuple					 =		o.T
            lowerCamelCase__    :      List[Any]					 =		q.T
            lowerCamelCase__    :      int					 =		v.T

            # Block i, layer 2 (MLP).
            lowerCamelCase__    :      Optional[Any]					 =		tax_layer_norm_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'decoder'						,	'pre_mlp_layer_norm' )
            lowerCamelCase__ ,							lowerCamelCase__    :      List[Any]					 =		tax_mlp_lookup(_UpperCAmelCase						,	_UpperCAmelCase						,	'decoder'						,	_UpperCAmelCase )
            lowerCamelCase__    :      List[str]					 =		layer_norm
            if split_mlp_wi:
                lowerCamelCase__    :      str					 =		wi[0].T
                lowerCamelCase__    :      str					 =		wi[1].T
            else:
                lowerCamelCase__    :      Any					 =		wi.T
            lowerCamelCase__    :      Optional[Any]					 =		wo.T

        lowerCamelCase__    :      int					 =		old['decoder/decoder_norm/scale']
        lowerCamelCase__    :      Optional[int]					 =		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:
            lowerCamelCase__    :      Dict					 =		old['decoder/logits_dense/kernel'].T

    return new


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    lowerCamelCase__    :      int					 =		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:
        lowerCamelCase__    :      Optional[int]					 =		state_dict['shared.weight']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            lowerCamelCase__    :      Union[str, Any]					 =		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.' )
            lowerCamelCase__    :      Optional[int]					 =		state_dict['shared.weight']

    return state_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Any					 =		checkpoints.load_tax_checkpoint(_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		convert_tax_to_pytorch(_UpperCAmelCase						,	num_layers=config.num_layers						,	is_encoder_only=_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		make_state_dict(_UpperCAmelCase						,	_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = False )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		TaConfig.from_json_file(_UpperCAmelCase )
    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:
        lowerCamelCase__    :      List[str]					 =		TaEncoderModel(_UpperCAmelCase )
    else:
        lowerCamelCase__    :      Tuple					 =		TaForConditionalGeneration(_UpperCAmelCase )

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

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

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


if __name__ == "__main__":
    _UpperCAmelCase				:       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
    )
    _UpperCAmelCase				:       Tuple	   =				parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )












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


_UpperCAmelCase				:       Optional[Any]	   =				"""▁"""

_UpperCAmelCase				:       Optional[Any]	   =				get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		BertGenerationTokenizer
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		True
 def      A_	(      self  :      List[Any]	)			->	List[str]:
     super().setUp()

     lowerCamelCase__    :      Dict					 =		BertGenerationTokenizer(UpperCAmelCase			,     keep_accents=UpperCAmelCase	)
     tokenizer.save_pretrained(self.tmpdirname	)
 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     lowerCamelCase__    :      List[str]					 =		'<s>'
     lowerCamelCase__    :      Optional[int]					 =		1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase	)			,     UpperCAmelCase	)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase	)			,     UpperCAmelCase	)
 def      A_	(      self  :      List[str]	)			->	Optional[int]:
     lowerCamelCase__    :      Optional[Any]					 =		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(UpperCAmelCase	)			,     1002	)
 def      A_	(      self  :      List[Any]	)			->	Optional[Any]:
     self.assertEqual(self.get_tokenizer().vocab_size			,     1000	)
 def      A_	(      self  :      Union[str, Any]	)			->	List[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		BertGenerationTokenizer(UpperCAmelCase			,     keep_accents=UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		tokenizer.tokenize('This is a test'	)
     self.assertListEqual(UpperCAmelCase			,     ['▁This', '▁is', '▁a', '▁t', 'est']	)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(UpperCAmelCase	)			,     [285, 46, 10, 170, 382]			,     )

     lowerCamelCase__    :      List[str]					 =		tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
     self.assertListEqual(
         UpperCAmelCase			,     [
             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',
             '.',
         ]			,     )
     lowerCamelCase__    :      Optional[int]					 =		tokenizer.convert_tokens_to_ids(UpperCAmelCase	)
     self.assertListEqual(
         UpperCAmelCase			,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,     )

     lowerCamelCase__    :      Optional[Any]					 =		tokenizer.convert_ids_to_tokens(UpperCAmelCase	)
     self.assertListEqual(
         UpperCAmelCase			,     [
             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  :      Dict	)			->	Tuple:
     return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'	)
 @slow
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     lowerCamelCase__    :      Union[str, Any]					 =		'Hello World!'
     lowerCamelCase__    :      Dict					 =		[18536, 2260, 101]

     self.assertListEqual(UpperCAmelCase			,     self.big_tokenizer.encode(UpperCAmelCase	)	)
 @slow
 def      A_	(      self  :      Optional[Any]	)			->	str:
     lowerCamelCase__    :      List[Any]					 =		(
         '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'
     )
     lowerCamelCase__    :      Any					 =		[
         871,
         419,
         358,
         946,
         991,
         2521,
         452,
         358,
         1357,
         387,
         7751,
         3536,
         112,
         985,
         456,
         126,
         865,
         938,
         5400,
         5734,
         458,
         1368,
         467,
         786,
         2462,
         5246,
         1159,
         633,
         865,
         4519,
         457,
         582,
         852,
         2557,
         427,
         916,
         508,
         405,
         34324,
         497,
         391,
         408,
         11342,
         1244,
         385,
         100,
         938,
         985,
         456,
         574,
         362,
         12597,
         3200,
         3129,
         1172,
     ]

     self.assertListEqual(UpperCAmelCase			,     self.big_tokenizer.encode(UpperCAmelCase	)	)
 @require_torch
 @slow
 def      A_	(      self  :      int	)			->	Optional[Any]:
     import torch

     from transformers import BertGenerationConfig, BertGenerationEncoder

     # Build sequence
     lowerCamelCase__    :      str					 =		list(self.big_tokenizer.get_vocab().keys()	)[:10]
     lowerCamelCase__    :      int					 =		' '.join(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		self.big_tokenizer.encode_plus(UpperCAmelCase			,     return_tensors='pt'			,     return_token_type_ids=UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		self.big_tokenizer.batch_encode_plus(
         [sequence + ' ' + sequence]			,     return_tensors='pt'			,     return_token_type_ids=UpperCAmelCase	)

     lowerCamelCase__    :      Tuple					 =		BertGenerationConfig()
     lowerCamelCase__    :      Optional[Any]					 =		BertGenerationEncoder(UpperCAmelCase	)

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

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







 @slow
 def      A_	(      self  :      Optional[int]	)			->	List[Any]:
     # fmt: off
     lowerCamelCase__    :      Any					 =		{'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=UpperCAmelCase			,     model_name='google/bert_for_seq_generation_L-24_bbc_encoder'			,     revision='c817d1fd1be2ffa69431227a1fe320544943d4db'			,     )





