'''simple docstring'''

import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)



class       UpperCAmelCase  (						_UpperCAmelCase ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase=0.0_1     ,       __lowerCAmelCase=1000			)  ->  List[str]:
        lowercase__    :   List[Any]						= p_stop
        lowercase__    :   List[Any]						= max_length


       def __iter__(						self			)  ->  Optional[int]:
        lowercase__    :   List[str]						= 0
        lowercase__    :   Union[str, Any]						= False
        while not stop and count < self.max_length:
         yield count
         count += 1
         lowercase__    :   Optional[Any]						= random.random() < self.p_stop



class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=True			)  ->  List[str]:
        lowercase__    :   List[str]						= [
            BatchSamplerShard(lowercase_     ,       2     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)
            for i in range(2			)
        ]
        lowercase__    :   Dict						= [list(lowercase_			) for batch_sampler_shard in batch_sampler_shards]
        if not split_batches:
         self.assertListEqual([len(lowercase_			) for shard in batch_sampler_shards]     ,       [len(lowercase_			) for e in expected]			)
        self.assertListEqual(lowercase_     ,       lowercase_			)

       def _lowerCAmelCase(						self			)  ->  str:
        # Check the shards when the dataset is a round multiple of total batch size.
        lowercase__    :   Optional[Any]						= BatchSampler(range(24			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   int						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        lowercase__    :   Any						= BatchSampler(range(24			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        # Expected shouldn't change
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        # Check the shards when the dataset is a round multiple of batch size but not total batch size.
        lowercase__    :   Union[str, Any]						= BatchSampler(range(21			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Any						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        lowercase__    :   int						= BatchSampler(range(21			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Dict						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
        # num_processes batch.
        lowercase__    :   List[Any]						= BatchSampler(range(22			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   List[Any]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        lowercase__    :   Optional[int]						= BatchSampler(range(22			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Optional[int]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
        # num_processes batch.
        lowercase__    :   Optional[Any]						= BatchSampler(range(20			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   List[str]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        lowercase__    :   Union[str, Any]						= BatchSampler(range(20			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   List[str]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        # Check the shards when the dataset is very small.
        lowercase__    :   int						= BatchSampler(range(2			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Optional[Any]						= [[[0, 1, 0]], [[1, 0, 1]]]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

        lowercase__    :   Optional[int]						= BatchSampler(range(2			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Any						= [[], []]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_			)

       def _lowerCAmelCase(						self			)  ->  str:
        # Check the shards when the dataset is a round multiple of batch size.
        lowercase__    :   Optional[Any]						= BatchSampler(range(24			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   Union[str, Any]						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_			)

        lowercase__    :   Union[str, Any]						= BatchSampler(range(24			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        # Expected shouldn't change
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_			)

        # Check the shards when the dataset is not a round multiple of batch size.
        lowercase__    :   Union[str, Any]						= BatchSampler(range(22			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   Any						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_			)

        lowercase__    :   Dict						= BatchSampler(range(22			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   List[Any]						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_			)

        # Check the shards when the dataset is not a round multiple of batch size or num_processes.
        lowercase__    :   str						= BatchSampler(range(21			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   List[str]						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_			)

        lowercase__    :   str						= BatchSampler(range(21			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   str						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_			)

        # Check the shards when the dataset is very small.
        lowercase__    :   int						= BatchSampler(range(2			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   Tuple						= [[[0, 1]], [[0, 1]]]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_			)

        lowercase__    :   List[str]						= BatchSampler(range(2			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   List[str]						= [[], []]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_			)

       def _lowerCAmelCase(						self			)  ->  Dict:
        # Check the shards when the dataset is a round multiple of total batch size.
        lowercase__    :   List[str]						= BatchSampler(range(24			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Optional[Any]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   Tuple						= BatchSampler(range(24			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        # Expected shouldn't change
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        # Check the shards when the dataset is a round multiple of batch size but not total batch size.
        lowercase__    :   Optional[Any]						= BatchSampler(range(21			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   List[Any]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   Tuple						= BatchSampler(range(21			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Any						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
        # num_processes batch.
        lowercase__    :   Dict						= BatchSampler(range(22			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   List[str]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   Union[str, Any]						= BatchSampler(range(22			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   List[Any]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
        # num_processes batch.
        lowercase__    :   Optional[int]						= BatchSampler(range(20			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   int						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   Dict						= BatchSampler(range(20			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Optional[Any]						= [
            [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
            [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        # Check the shards when the dataset is very small.
        lowercase__    :   Tuple						= BatchSampler(range(2			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   List[str]						= [[[0, 1]], []]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   Optional[int]						= BatchSampler(range(2			)     ,       batch_size=3     ,       drop_last=lowercase_			)
        lowercase__    :   Any						= [[], []]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       even_batches=lowercase_			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        # Check the shards when the dataset is a round multiple of batch size.
        lowercase__    :   Tuple						= BatchSampler(range(24			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   Union[str, Any]						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   int						= BatchSampler(range(24			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        # Expected shouldn't change
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)

        # Check the shards when the dataset is not a round multiple of batch size.
        lowercase__    :   Dict						= BatchSampler(range(22			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   Any						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   Optional[int]						= BatchSampler(range(22			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   Optional[int]						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)

        # Check the shards when the dataset is not a round multiple of batch size or num_processes.
        lowercase__    :   Any						= BatchSampler(range(21			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   int						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   Union[str, Any]						= BatchSampler(range(21			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   int						= [
            [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
            [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
        ]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)

        # Check the shards when the dataset is very small.
        lowercase__    :   Optional[Any]						= BatchSampler(range(2			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   int						= [[[0, 1]], []]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)

        lowercase__    :   str						= BatchSampler(range(2			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   str						= [[], []]
        self.check_batch_sampler_shards(lowercase_     ,       lowercase_     ,       split_batches=lowercase_     ,       even_batches=lowercase_			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[str]						= [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
        lowercase__    :   str						= [BatchSamplerShard(lowercase_     ,       2     ,       lowercase_     ,       even_batches=lowercase_			) for i in range(2			)]

        self.assertEqual(len(batch_sampler_shards[0]			)     ,       3			)
        self.assertEqual(len(batch_sampler_shards[1]			)     ,       2			)

        self.assertListEqual(list(batch_sampler_shards[0]			)     ,       [[0, 1, 2], [5, 6, 7, 8], [12, 13]]			)
        self.assertListEqual(list(batch_sampler_shards[1]			)     ,       [[3, 4], [9, 10, 11]]			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=2     ,       __lowerCAmelCase=False			)  ->  List[str]:
        random.seed(lowercase_			)
        lowercase__    :   List[str]						= list(lowercase_			)

        lowercase__    :   Optional[Any]						= [
            IterableDatasetShard(
                lowercase_     ,       batch_size=lowercase_     ,       drop_last=lowercase_     ,       num_processes=lowercase_     ,       process_index=lowercase_     ,       split_batches=lowercase_     ,       )
            for i in range(lowercase_			)
        ]
        lowercase__    :   List[str]						= []
        for iterable_dataset_shard in iterable_dataset_shards:
         # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
         random.seed(lowercase_			)
         iterable_dataset_lists.append(list(lowercase_			)			)

        lowercase__    :   Dict						= batch_size // num_processes if split_batches else batch_size
        # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
        lowercase__    :   str						= iterable_dataset_lists[0]
        for l in iterable_dataset_lists[1:]:
         self.assertEqual(len(lowercase_			)     ,       len(lowercase_			)			)
         self.assertTrue(len(lowercase_			) % shard_batch_size == 0			)

        lowercase__    :   int						= []
        for idx in range(0     ,       len(lowercase_			)     ,       lowercase_			):
         for l in iterable_dataset_lists:
          observed += l[idx : idx + shard_batch_size]

        if not drop_last:
         while len(lowercase_			) < len(lowercase_			):
          reference += reference
        self.assertListEqual(lowercase_     ,       reference[: len(lowercase_			)]			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   Tuple						= 42
        lowercase__    :   Dict						= RandomIterableDataset()

        self.check_iterable_dataset_shards(lowercase_     ,       lowercase_     ,       batch_size=4     ,       drop_last=lowercase_     ,       split_batches=lowercase_			)
        self.check_iterable_dataset_shards(lowercase_     ,       lowercase_     ,       batch_size=4     ,       drop_last=lowercase_     ,       split_batches=lowercase_			)
        self.check_iterable_dataset_shards(lowercase_     ,       lowercase_     ,       batch_size=4     ,       drop_last=lowercase_     ,       split_batches=lowercase_			)
        self.check_iterable_dataset_shards(lowercase_     ,       lowercase_     ,       batch_size=4     ,       drop_last=lowercase_     ,       split_batches=lowercase_			)

        # Edge case with a very small dataset
        lowercase__    :   Tuple						= RandomIterableDataset(max_length=2			)

        self.check_iterable_dataset_shards(lowercase_     ,       lowercase_     ,       batch_size=4     ,       drop_last=lowercase_     ,       split_batches=lowercase_			)
        self.check_iterable_dataset_shards(lowercase_     ,       lowercase_     ,       batch_size=4     ,       drop_last=lowercase_     ,       split_batches=lowercase_			)
        self.check_iterable_dataset_shards(lowercase_     ,       lowercase_     ,       batch_size=4     ,       drop_last=lowercase_     ,       split_batches=lowercase_			)
        self.check_iterable_dataset_shards(lowercase_     ,       lowercase_     ,       batch_size=4     ,       drop_last=lowercase_     ,       split_batches=lowercase_			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[Any]						= BatchSampler(range(16			)     ,       batch_size=4     ,       drop_last=lowercase_			)
        lowercase__    :   int						= SkipBatchSampler(lowercase_     ,       2			)
        self.assertListEqual(list(lowercase_			)     ,       [[8, 9, 10, 11], [12, 13, 14, 15]]			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   int						= SkipDataLoader(list(range(16			)			)     ,       batch_size=4     ,       skip_batches=2			)
        self.assertListEqual([t.tolist() for t in dataloader]     ,       [[8, 9, 10, 11], [12, 13, 14, 15]]			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Optional[Any]						= DataLoader(list(range(16			)			)     ,       batch_size=4			)
        lowercase__    :   int						= skip_first_batches(lowercase_     ,       num_batches=2			)
        self.assertListEqual([t.tolist() for t in new_dataloader]     ,       [[8, 9, 10, 11], [12, 13, 14, 15]]			)

       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   int						= DataLoaderShard(list(range(16			)			)     ,       batch_size=4			)
        for idx, _ in enumerate(lowercase_			):
         self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3			)

        # Test it also works on the second iteration
        for idx, _ in enumerate(lowercase_			):
         self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3			)


       def _lowerCAmelCase(						self			)  ->  int:
        Accelerator()
        lowercase__    :   Tuple						= DataLoaderDispatcher(range(16			)     ,       batch_size=4			)
        for idx, _ in enumerate(lowercase_			):
         self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3			)

        # Test it also works on the second iteration
        for idx, _ in enumerate(lowercase_			):
         self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3			)


'''simple docstring'''

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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase="attention"					):
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
 lowercase__    :   Any						= params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
 return k, o, q, v
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=False					):
 if split_mlp_wi:
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
  lowercase__    :   Dict						= (wi_a, wi_a)
 else:
  lowercase__    :   List[Any]						= params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
 return wi, wo
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]
def       __UpperCamelCase	(       UpperCAmelCase		,					*, UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= traverse_util.flatten_dict(variables['''target''']					)
 lowercase__    :   Any						= {'''/'''.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
 lowercase__    :   List[str]						= '''encoder/layers_0/mlp/wi_0/kernel''' in old
 print('''Split MLP:'''		,					UpperCAmelCase					)

 lowercase__    :   Union[str, Any]						= collections.OrderedDict()

 # Shared embeddings.
 lowercase__    :   str						= old['''token_embedder/embedding''']

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

  # Block i, layer 1 (MLP).
  lowercase__    :   List[str]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					'''pre_mlp_layer_norm'''					)
  lowercase__			,							lowercase__    :   List[Any]						= tax_mlp_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					UpperCAmelCase					)
  lowercase__    :   int						= layer_norm
  if split_mlp_wi:
   lowercase__    :   Union[str, Any]						= wi[0].T
   lowercase__    :   List[str]						= wi[1].T
  else:
   lowercase__    :   int						= wi.T
  lowercase__    :   List[str]						= wo.T

 lowercase__    :   Dict						= old[
     '''encoder/relpos_bias/rel_embedding'''
 ].T
 lowercase__    :   str						= old['''encoder/encoder_norm/scale''']

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

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

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

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

 return new
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   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:
  lowercase__    :   Optional[int]						= state_dict['''shared.weight''']

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

 return state_dict
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[str]						= checkpoints.load_tax_checkpoint(UpperCAmelCase					)
 lowercase__    :   str						= convert_tax_to_pytorch(UpperCAmelCase		,					num_layers=config.num_layers		,					is_encoder_only=UpperCAmelCase					)
 lowercase__    :   List[Any]						= make_state_dict(UpperCAmelCase		,					UpperCAmelCase					)
 model.load_state_dict(UpperCAmelCase		,					strict=UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = False					):
 lowercase__    :   Dict						= 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:
  lowercase__    :   List[str]						= TaEncoderModel(UpperCAmelCase					)
 else:
  lowercase__    :   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__":
  __a:			Optional[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
  )
  __a:			Tuple	       =       parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )

