





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      List[str]					 =		AutoConfig.from_pretrained(_UpperCAmelCase )
    lowerCamelCase__    :      Any					 =		FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCAmelCase )
    lowerCamelCase__    :      Optional[int]					 =		checkpoints.load_tax_checkpoint(_UpperCAmelCase )

    lowerCamelCase__    :      Dict					 =		'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

    if config.model_type == "t5":
        lowerCamelCase__    :      Optional[Any]					 =		'SelfAttention'
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        lowerCamelCase__    :      Union[str, Any]					 =		'LocalSelfAttention'
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        lowerCamelCase__    :      Tuple					 =		'TransientGlobalSelfAttention'
    else:
        raise ValueError(
            'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
            ' attribute with a value from [\'local\', \'transient-global].' )

    # Encoder
    for layer_index in range(config.num_layers ):
        lowerCamelCase__    :      Union[str, Any]					 =		F"""layers_{str(_UpperCAmelCase )}"""

        # Self-Attention
        lowerCamelCase__    :      Any					 =		tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
        lowerCamelCase__    :      List[str]					 =		tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
        lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
        lowerCamelCase__    :      Tuple					 =		tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCamelCase__    :      Optional[Any]					 =		tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

        # Layer Normalization
        lowerCamelCase__    :      int					 =		tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

        if split_mlp_wi:
            lowerCamelCase__    :      Tuple					 =		tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
            lowerCamelCase__    :      Optional[int]					 =		tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
        else:
            lowerCamelCase__    :      List[str]					 =		tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

        lowerCamelCase__    :      int					 =		tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

        # Layer Normalization
        lowerCamelCase__    :      Optional[Any]					 =		tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

        # Assigning
        lowerCamelCase__    :      Optional[int]					 =		flax_model.params['encoder']['block'][str(_UpperCAmelCase )]['layer']
        lowerCamelCase__    :      int					 =		tax_attention_key
        lowerCamelCase__    :      str					 =		tax_attention_out
        lowerCamelCase__    :      Optional[Any]					 =		tax_attention_query
        lowerCamelCase__    :      Dict					 =		tax_attention_value

        lowerCamelCase__    :      List[str]					 =		tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCamelCase__    :      List[Any]					 =		tax_global_layer_norm

        if split_mlp_wi:
            lowerCamelCase__    :      List[str]					 =		tax_mlp_wi_a
            lowerCamelCase__    :      Union[str, Any]					 =		tax_mlp_wi_a
        else:
            lowerCamelCase__    :      List[Any]					 =		tax_mlp_wi

        lowerCamelCase__    :      List[str]					 =		tax_mlp_wo
        lowerCamelCase__    :      Optional[int]					 =		tax_mlp_layer_norm

        lowerCamelCase__    :      Any					 =		flax_model_encoder_layer_block

    # Only for layer 0:
    lowerCamelCase__    :      List[Any]					 =		tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
    lowerCamelCase__    :      List[str]					 =		tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        lowerCamelCase__    :      Optional[Any]					 =		tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
        lowerCamelCase__    :      str					 =		tax_encoder_global_rel_embedding

    # Assigning
    lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['encoder']['encoder_norm']['scale']
    lowerCamelCase__    :      str					 =		tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers ):
        lowerCamelCase__    :      str					 =		F"""layers_{str(_UpperCAmelCase )}"""

        # Self-Attention
        lowerCamelCase__    :      Optional[int]					 =		tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
        lowerCamelCase__    :      List[Any]					 =		tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
        lowerCamelCase__    :      str					 =		tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
        lowerCamelCase__    :      Tuple					 =		tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

        # Layer Normalization
        lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
            'scale'
        ]

        # Encoder-Decoder-Attention
        lowerCamelCase__    :      Optional[Any]					 =		tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
        lowerCamelCase__    :      str					 =		tax_enc_dec_attention_module['key']['kernel']
        lowerCamelCase__    :      str					 =		tax_enc_dec_attention_module['out']['kernel']
        lowerCamelCase__    :      int					 =		tax_enc_dec_attention_module['query']['kernel']
        lowerCamelCase__    :      str					 =		tax_enc_dec_attention_module['value']['kernel']

        # Layer Normalization
        lowerCamelCase__    :      Dict					 =		tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

        # MLP
        if split_mlp_wi:
            lowerCamelCase__    :      Optional[Any]					 =		tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
            lowerCamelCase__    :      str					 =		tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
        else:
            lowerCamelCase__    :      List[str]					 =		tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

        lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

        # Layer Normalization
        lowerCamelCase__    :      List[str]					 =		tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

        # Assigning
        lowerCamelCase__    :      Tuple					 =		flax_model.params['decoder']['block'][str(_UpperCAmelCase )]['layer']
        lowerCamelCase__    :      Optional[Any]					 =		tax_attention_key
        lowerCamelCase__    :      str					 =		tax_attention_out
        lowerCamelCase__    :      int					 =		tax_attention_query
        lowerCamelCase__    :      Optional[Any]					 =		tax_attention_value

        lowerCamelCase__    :      Optional[Any]					 =		tax_pre_attention_layer_norm

        lowerCamelCase__    :      str					 =		tax_enc_dec_attention_key
        lowerCamelCase__    :      Dict					 =		tax_enc_dec_attention_out
        lowerCamelCase__    :      Optional[Any]					 =		tax_enc_dec_attention_query
        lowerCamelCase__    :      Optional[int]					 =		tax_enc_dec_attention_value

        lowerCamelCase__    :      Tuple					 =		tax_cross_layer_norm

        if split_mlp_wi:
            lowerCamelCase__    :      List[Any]					 =		tax_mlp_wi_a
            lowerCamelCase__    :      Union[str, Any]					 =		tax_mlp_wi_a
        else:
            lowerCamelCase__    :      str					 =		tax_mlp_wi

        lowerCamelCase__    :      Optional[int]					 =		tax_mlp_wo

        lowerCamelCase__    :      Dict					 =		txa_mlp_layer_norm

        lowerCamelCase__    :      List[Any]					 =		flax_model_decoder_layer_block

    # Decoder Normalization
    lowerCamelCase__    :      str					 =		tax_model['target']['decoder']['decoder_norm']['scale']
    lowerCamelCase__    :      List[Any]					 =		txa_decoder_norm

    # Only for layer 0:
    lowerCamelCase__    :      Any					 =		tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
    lowerCamelCase__    :      List[str]					 =		tax_decoder_rel_embedding

    # Token Embeddings
    lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['token_embedder']['embedding']
    lowerCamelCase__    :      Optional[Any]					 =		txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        lowerCamelCase__    :      List[str]					 =		tax_model['target']['decoder']['logits_dense']['kernel']

    flax_model.save_pretrained(_UpperCAmelCase )
    print('T5X Model was sucessfully converted!' )


if __name__ == "__main__":
    _UpperCAmelCase				:       int	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
    )
    parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
    parser.add_argument(
        """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
    )
    _UpperCAmelCase				:       Tuple	   =				parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










'''simple docstring'''

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Optional[int]   				=					BarthezTokenizer
							lowerCAmelCase       :    int   				=					BarthezTokenizerFast
							lowerCAmelCase       :    Dict   				=					True
							lowerCAmelCase       :    str   				=					True







							def 							__lowercase			(							self					:       List[Any]		):
									super().setUp()

									_a     :      List[Any]        =							BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'		)
									tokenizer.save_pretrained(self.tmpdirname		)
									tokenizer.save_pretrained(self.tmpdirname	,legacy_format=_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							tokenizer







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      Optional[Any]        =							'<pad>'
									_a     :      List[Any]        =							1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase		)	,_UpperCAmelCase		)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase		)	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									_a     :      Any        =							list(self.get_tokenizer().get_vocab().keys()		)

									self.assertEqual(vocab_keys[0]	,'<s>'		)
									self.assertEqual(vocab_keys[1]	,'<pad>'		)
									self.assertEqual(vocab_keys[-1]	,'<mask>'		)
									self.assertEqual(len(_UpperCAmelCase		)	,101122		)







							def 							__lowercase			(							self					:       Dict		):
									self.assertEqual(self.get_tokenizer().vocab_size	,101122		)







							@require_torch
							def 							__lowercase			(							self					:       Dict		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Dict        =							[0, 57, 3018, 70307, 91, 2]

									_a     :      Dict        =							self.tokenizer(
									    _UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

									self.assertEqual((2, 6)	,batch.input_ids.shape		)
									self.assertEqual((2, 6)	,batch.attention_mask.shape		)
									_a     :      Tuple        =							batch.input_ids.tolist()[0]
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									if not self.test_rust_tokenizer:
											return

									_a     :      str        =							self.get_tokenizer()
									_a     :      List[str]        =							self.get_rust_tokenizer()

									_a     :      Dict        =							'I was born in 92000, and this is falsé.'

									_a     :      List[Any]        =							tokenizer.tokenize(_UpperCAmelCase		)
									_a     :      Tuple        =							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Union[str, Any]        =							self.get_rust_tokenizer()
									_a     :      Any        =							tokenizer.encode(_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[int]		):
									# fmt: off
									_a     :      Optional[int]        =							{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									_a     :      Optional[Any]        =							[
									    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
									    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
									    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
									    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
									    'telles que la traduction et la synthèse de texte.',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase	,model_name='moussaKam/mbarthez'	,revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,sequences=_UpperCAmelCase	,)




