

"""simple docstring"""







import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCamelCase_     :     List[str]    		=						logging.get_logger(__name__)






@dataclass
class    __A       :






 """simple docstring"""




 __lowerCAmelCase				      =						42
 __lowerCAmelCase				      =						42
 __lowerCAmelCase				      =						None
 __lowerCAmelCase				      =						None


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     return json.dumps(dataclasses.asdict(self		)				,					indent=2		) + "\n"






@dataclass(frozen=UpperCAmelCase_    )
class    __A       :






 """simple docstring"""




 __lowerCAmelCase				      =						42
 __lowerCAmelCase				      =						None
 __lowerCAmelCase				      =						None
 __lowerCAmelCase				      =						None


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
     return json.dumps(dataclasses.asdict(self		)		) + "\n"






class    __A       :






 """simple docstring"""




 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
     raise NotImplementedError()


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
     raise NotImplementedError()


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Dict:
     raise NotImplementedError()


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Optional[int]:
     raise NotImplementedError()


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     raise NotImplementedError()


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  int:
     if len(self.get_labels()		) > 1:
         a					=self.get_labels()[int(example.label		)]
     return example





 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None		)      ->  int:
     with open(__A				,					'''r'''				,					encoding='''utf-8-sig'''		) as f:
         return list(csv.reader(__A				,					delimiter='''\t'''				,					quotechar=__A		)		)






class    __A       ( UpperCAmelCase_    ):






 """simple docstring"""




 def __init__(	self				,					__A=None				,					__A=None				,					__A="classification"				,					__A=False		)      ->  Optional[Any]:
     a					=[] if labels is None else labels
     a					=[] if examples is None else examples
     a					=mode
     a					=verbose


 def __len__(	self		)      ->  List[str]:
     return len(self.examples		)


 def __getitem__(	self				,					__A		)      ->  str:
     if isinstance(__A				,					__A		):
         return SingleSentenceClassificationProcessor(labels=self.labels				,					examples=self.examples[idx]		)
     return self.examples[idx]


 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=""				,					__A=0				,					__A=1				,					__A=None				,					__A=False				,					**__A		)      ->  Any:
     a					=cls(**__A		)
     processor.add_examples_from_csv(
         __A				,					split_name=__A				,					column_label=__A				,					column_text=__A				,					column_id=__A				,					skip_first_row=__A				,					overwrite_labels=__A				,					overwrite_examples=__A				,					)
     return processor


 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None				,					**__A		)      ->  Union[str, Any]:
     a					=cls(**__A		)
     processor.add_examples(__A				,					labels=__A		)
     return processor


 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A=""				,					__A=0				,					__A=1				,					__A=None				,					__A=False				,					__A=False				,					__A=False				,					)      ->  int:
     a					=self._read_tsv(__A		)
     if skip_first_row:
         a					=lines[1:]
     a					=[]
     a					=[]
     a					=[]
     for i, line in enumerate(__A		):
         texts.append(line[column_text]		)
         labels.append(line[column_label]		)
         if column_id is not None:
             ids.append(line[column_id]		)
         else:
             a					=f'''{split_name}-{i}''' if split_name else str(__A		)
             ids.append(__A		)

     return self.add_examples(
         __A				,					__A				,					__A				,					overwrite_labels=__A				,					overwrite_examples=__A		)


 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A=None				,					__A=None				,					__A=False				,					__A=False		)      ->  Any:
     if labels is not None and len(__A		) != len(__A		):
         raise ValueError(
             f'''Text and labels have mismatched lengths {len(__A		)} and {len(__A		)}'''		)
     if ids is not None and len(__A		) != len(__A		):
         raise ValueError(f'''Text and ids have mismatched lengths {len(__A		)} and {len(__A		)}'''		)
     if ids is None:
         a					=[None] * len(__A		)
     if labels is None:
         a					=[None] * len(__A		)
     a					=[]
     a					=set()
     for text_or_text_and_label, label, guid in zip(__A				,					__A				,					__A		):
         if isinstance(__A				,					(tuple, list)		) and label is None:
             a	,   a					=text_or_text_and_label
         else:
             a					=text_or_text_and_label
         added_labels.add(__A		)
         examples.append(InputExample(guid=__A				,					text_a=__A				,					text_b=__A				,					label=__A		)		)

     # Update examples
     if overwrite_examples:
         a					=examples
     else:
         self.examples.extend(__A		)

     # Update labels
     if overwrite_labels:
         a					=list(__A		)
     else:
         a					=list(set(self.labels		).union(__A		)		)

     return self.examples





 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A=None				,					__A=False				,					__A=0				,					__A=True				,					__A=None				,					)      ->  Any:
     if max_length is None:
         a					=tokenizer.max_len

     a					={label: i for i, label in enumerate(self.labels		)}

     a					=[]
     for ex_index, example in enumerate(self.examples		):
         if ex_index % 1_0000 == 0:
             logger.info(f'''Tokenizing example {ex_index}'''		)

         a					=tokenizer.encode(
             example.text_a				,					add_special_tokens=__A				,					max_length=min(__A				,					tokenizer.max_len		)				,					)
         all_input_ids.append(__A		)

     a					=max(len(__A		) for input_ids in all_input_ids		)

     a					=[]
     for ex_index, (input_ids, example) in enumerate(zip(__A				,					self.examples		)		):
         if ex_index % 1_0000 == 0:
             logger.info(f'''Writing example {ex_index}/{len(self.examples		)}'''		)
         # The mask has 1 for real tokens and 0 for padding tokens. Only real
         # tokens are attended to.
         a					=[1 if mask_padding_with_zero else 0] * len(__A		)

         # Zero-pad up to the sequence length.
         a					=batch_length - len(__A		)
         if pad_on_left:
             a					=([pad_token] * padding_length) + input_ids
             a					=([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
         else:
             a					=input_ids + ([pad_token] * padding_length)
             a					=attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

         if len(__A		) != batch_length:
             raise ValueError(f'''Error with input length {len(__A		)} vs {batch_length}'''		)
         if len(__A		) != batch_length:
             raise ValueError(f'''Error with input length {len(__A		)} vs {batch_length}'''		)

         if self.mode == "classification":
             a					=label_map[example.label]
         elif self.mode == "regression":
             a					=float(example.label		)
         else:
             raise ValueError(self.mode		)

         if ex_index < 5 and self.verbose:
             logger.info('''*** Example ***'''		)
             logger.info(f'''guid: {example.guid}'''		)
             logger.info(f'''input_ids: {" ".join([str(__A		) for x in input_ids]		)}'''		)
             logger.info(f'''attention_mask: {" ".join([str(__A		) for x in attention_mask]		)}'''		)
             logger.info(f'''label: {example.label} (id = {label})'''		)

         features.append(InputFeatures(input_ids=__A				,					attention_mask=__A				,					label=__A		)		)

     if return_tensors is None:
         return features
     elif return_tensors == "tf":
         if not is_tf_available():
             raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''		)
         import tensorflow as tf

         def gen():
             for ex in features:
                 yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

         a					=tf.data.Dataset.from_generator(
             __A				,					({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)				,					({'''input_ids''': tf.TensorShape([None]		), '''attention_mask''': tf.TensorShape([None]		)}, tf.TensorShape([]		))				,					)
         return dataset
     elif return_tensors == "pt":
         if not is_torch_available():
             raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''		)
         import torch
         from torch.utils.data import TensorDataset

         a					=torch.tensor([f.input_ids for f in features]				,					dtype=torch.long		)
         a					=torch.tensor([f.attention_mask for f in features]				,					dtype=torch.long		)
         if self.mode == "classification":
             a					=torch.tensor([f.label for f in features]				,					dtype=torch.long		)
         elif self.mode == "regression":
             a					=torch.tensor([f.label for f in features]				,					dtype=torch.float		)

         a					=TensorDataset(__A				,					__A				,					__A		)
         return dataset
     else:
         raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''		)



"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=TaConfig.from_pretrained(lowercase	)
    a					=FlaxTaForConditionalGeneration(config=lowercase	)
    a					=checkpoints.load_tax_checkpoint(lowercase	)

    a					='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    # Encoder
    for layer_index in range(config.num_layers	):
        a					=f'''layers_{str(lowercase	)}'''

        # Self-Attention
        a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Layer Normalization
        a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        a					=tax_attention_key
        a					=tax_attention_out
        a					=tax_attention_query
        a					=tax_attention_value

        a					=tax_attention_layer_norm

        if split_mlp_wi:
            a					=tax_mlp_wi_a
            a					=tax_mlp_wi_a
        else:
            a					=tax_mlp_wi

        a					=tax_mlp_wo
        a					=tax_mlp_layer_norm

    # Only for layer 0:
    a					=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    a					=tax_encoder_rel_embedding

    # Assigning
    a					=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    a					=tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers	):
        a					=f'''layers_{str(lowercase	)}'''

        # Self-Attention
        a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
            '''kernel'''
        ]
        a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
            '''kernel'''
        ]
        a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
            '''kernel'''
        ]
        a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
            '''kernel'''
        ]

        # Layer Normalization
        a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        a					=tax_attention_key
        a					=tax_attention_out
        a					=tax_attention_query
        a					=tax_attention_value

        a					=tax_pre_attention_layer_norm

        a					=tax_enc_dec_attention_key
        a					=tax_enc_dec_attention_out
        a					=tax_enc_dec_attention_query
        a					=tax_enc_dec_attention_value

        a					=tax_cross_layer_norm

        if split_mlp_wi:
            a					=tax_mlp_wi_a
            a					=tax_mlp_wi_a
        else:
            a					=tax_mlp_wi

        a					=tax_mlp_wo

        a					=txa_mlp_layer_norm

    # Decoder Normalization
    a					=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    a					=txa_decoder_norm

    # Only for layer 0:
    a					=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    a					=tax_decoder_rel_embedding

    # Token Embeddings
    a					=tax_model['''target''']['''token_embedder''']['''embedding''']
    a					=txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        a					=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
       lowerCamelCase_     :     Dict    		=						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
       )
       parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
       parser.add_argument(
           """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
       )
       lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)