
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_    (     A__				,	A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

    return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def 		lowercase_    (     A__				,	A__				,	A__				,	A__="attention"	)							->							str:
    """simple docstring"""

    snake_case				  =      snake_case				  =      np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]	)
    snake_case				  =      k_tmp.reshape(k_tmp.shape[0]				,	k_tmp.shape[1] * k_tmp.shape[2]	)
    snake_case				  =      np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]	)
    snake_case				  =      o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,	o_tmp.shape[2]	)
    snake_case				  =      np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]	)
    snake_case				  =      q_tmp.reshape(q_tmp.shape[0]				,	q_tmp.shape[1] * q_tmp.shape[2]	)
    snake_case				  =      np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]	)
    snake_case				  =      v_tmp.reshape(v_tmp.shape[0]				,	v_tmp.shape[1] * v_tmp.shape[2]	)
    return k, o, q, v



def 		lowercase_    (     A__				,	A__				,	A__				,	A__=False	)							->							Union[str, Any]:
    """simple docstring"""

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

    snake_case				  =      params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
    return wi, wo



def 		lowercase_    (     A__				,	A__				,	A__				,	A__	)							->							Dict:
    """simple docstring"""

    return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def 		lowercase_    (     A__				,	*, A__				,	A__				,	A__ = False	)							->							int:
    """simple docstring"""

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

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

    snake_case				  =      collections.OrderedDict()

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

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

        # Block i, layer 1 (MLP).
        snake_case				  =      tax_layer_norm_lookup(A__				,	A__				,	"encoder"				,	"pre_mlp_layer_norm"	)
        snake_case  ,							snake_case				  =      tax_mlp_lookup(A__				,	A__				,	"encoder"				,	A__	)
        snake_case				  =      layer_norm
        if split_mlp_wi:
            snake_case				  =      wi[0].T
            snake_case				  =      wi[1].T
        else:
            snake_case				  =      wi.T
        snake_case				  =      wo.T
        if scalable_attention:
            # convert the rel_embedding of each layer
            snake_case				  =      tax_relpos_bias_lookup(
                A__				,	A__				,	"encoder"	).T

    snake_case				  =      old["encoder/encoder_norm/scale"]

    if not scalable_attention:
        snake_case				  =      tax_relpos_bias_lookup(
            A__				,	0				,	"encoder"	).T
        snake_case				  =      tax_relpos_bias_lookup(
            A__				,	0				,	"decoder"	).T

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

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

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

            if scalable_attention:
                # convert the rel_embedding of each layer
                snake_case				  =      tax_relpos_bias_lookup(A__				,	A__				,	"decoder"	).T

        snake_case				  =      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:
            snake_case				  =      old["decoder/logits_dense/kernel"].T

    return new



def 		lowercase_    (     A__				,	A__	)							->							Any:
    """simple docstring"""

    snake_case				  =      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:
        snake_case				  =      state_dict["shared.weight"]

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

    return state_dict



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							str:
    """simple docstring"""

    snake_case				  =      checkpoints.load_tax_checkpoint(A__	)
    snake_case				  =      convert_tax_to_pytorch(
        A__				,	num_layers=config.num_layers				,	is_encoder_only=A__				,	scalable_attention=A__	)
    snake_case				  =      make_state_dict(A__				,	A__	)
    model.load_state_dict(A__				,	strict=A__	)



def 		lowercase_    (     A__				,	A__				,	A__				,	A__ = False				,	A__ = False				,	)							->							int:
    """simple docstring"""

    snake_case				  =      MTaConfig.from_json_file(A__	)
    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:
        snake_case				  =      UMTaEncoderModel(A__	)
    else:
        snake_case				  =      UMTaForConditionalGeneration(A__	)

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

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

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


if __name__ == "__main__":
   _A						      =				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						      =				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,
   )



import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							str     =	PhobertTokenizer
     UpperCAmelCase__       :							List[Any]     =	False





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         snake_case				  =      ["T@@", "i", "I", "R@@", "r", "e@@"]
         snake_case				  =      dict(zip(_A       ,					range(len(_A     )     )     )     )
         snake_case				  =      ["#version: 0.2", "l à</w>"]
         snake_case				  =      {"unk_token": "<unk>"}

         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["vocab_file"]     )
         snake_case				  =      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 	UpperCAmelCase(self   :					Optional[int]       ,					**_A   :					Optional[int]     )		->		Union[str, Any]:
         kwargs.update(self.special_tokens_map     )
         return PhobertTokenizer.from_pretrained(self.tmpdirname       ,					**_A     )





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[Any]     )		->		str:
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





     def 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         snake_case				  =      PhobertTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map     )
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         snake_case				  =      tokenizer.tokenize(_A     )
         print(_A     )
         self.assertListEqual(_A       ,					_A     )

         snake_case				  =      tokens + [tokenizer.unk_token]

         snake_case				  =      [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     )

