
'''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__						(       A ):



    def __init__(	self      :       Optional[int],__A      :       Union[str, Any]=0.01,__A      :       str=1_0_0_0     ):
          _lowerCamelCase				:   Any								=   p_stop
          _lowerCamelCase				:   Tuple								=   max_length



    def __iter__(	self      :       Tuple     ):
          _lowerCamelCase				:   int								=   0
          _lowerCamelCase				:   str								=   False
          while not stop and count < self.max_length:
                yield count
                count += 1
                _lowerCamelCase				:   List[Any]								=   random.random() < self.p_stop


class  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       str,__A      :       List[str],__A      :       List[str],__A      :       Union[str, Any]=False,__A      :       Dict=True     ):
          _lowerCamelCase				:   Tuple								=   [
              BatchSamplerShard(__A,2,__A,split_batches=__A,even_batches=__A     )
              for i in range(2     )
          ]
          _lowerCamelCase				:   Dict								=   [list(__A     ) for batch_sampler_shard in batch_sampler_shards]
          if not split_batches:
                self.assertListEqual([len(__A     ) for shard in batch_sampler_shards],[len(__A     ) for e in expected]     )
          self.assertListEqual(__A,__A     )



    def   lowerCamelCase_  (	self      :       Any     ):
          # Check the shards when the dataset is a round multiple of total batch size.
          _lowerCamelCase				:   int								=   BatchSampler(range(2_4     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
          ]
          self.check_batch_sampler_shards(__A,__A     )

          _lowerCamelCase				:   Any								=   BatchSampler(range(2_4     ),batch_size=3,drop_last=__A     )
          # Expected shouldn't change
          self.check_batch_sampler_shards(__A,__A     )

          # Check the shards when the dataset is a round multiple of batch size but not total batch size.
          _lowerCamelCase				:   int								=   BatchSampler(range(2_1     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   int								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
          ]
          self.check_batch_sampler_shards(__A,__A     )

          _lowerCamelCase				:   Union[str, Any]								=   BatchSampler(range(2_1     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   str								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
          ]
          self.check_batch_sampler_shards(__A,__A     )

          # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
          # num_processes batch.
          _lowerCamelCase				:   Optional[int]								=   BatchSampler(range(2_2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   List[Any]								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
          ]
          self.check_batch_sampler_shards(__A,__A     )

          _lowerCamelCase				:   Any								=   BatchSampler(range(2_2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
          ]
          self.check_batch_sampler_shards(__A,__A     )

          # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
          # num_processes batch.
          _lowerCamelCase				:   Any								=   BatchSampler(range(2_0     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   int								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
          ]
          self.check_batch_sampler_shards(__A,__A     )

          _lowerCamelCase				:   Tuple								=   BatchSampler(range(2_0     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
          ]
          self.check_batch_sampler_shards(__A,__A     )

          # Check the shards when the dataset is very small.
          _lowerCamelCase				:   Dict								=   BatchSampler(range(2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   List[Any]								=   [[[0, 1, 0]], [[1, 0, 1]]]
          self.check_batch_sampler_shards(__A,__A     )

          _lowerCamelCase				:   Tuple								=   BatchSampler(range(2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Dict								=   [[], []]
          self.check_batch_sampler_shards(__A,__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          # Check the shards when the dataset is a round multiple of batch size.
          _lowerCamelCase				:   Any								=   BatchSampler(range(2_4     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Dict								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )

          _lowerCamelCase				:   Optional[Any]								=   BatchSampler(range(2_4     ),batch_size=4,drop_last=__A     )
          # Expected shouldn't change
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size.
          _lowerCamelCase				:   int								=   BatchSampler(range(2_2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   List[str]								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )

          _lowerCamelCase				:   Union[str, Any]								=   BatchSampler(range(2_2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size or num_processes.
          _lowerCamelCase				:   Any								=   BatchSampler(range(2_1     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Dict								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )

          _lowerCamelCase				:   List[Any]								=   BatchSampler(range(2_1     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )

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

          _lowerCamelCase				:   Dict								=   BatchSampler(range(2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   int								=   [[], []]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          # Check the shards when the dataset is a round multiple of total batch size.
          _lowerCamelCase				:   List[Any]								=   BatchSampler(range(2_4     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
          ]
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

          _lowerCamelCase				:   str								=   BatchSampler(range(2_4     ),batch_size=3,drop_last=__A     )
          # Expected shouldn't change
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

          # Check the shards when the dataset is a round multiple of batch size but not total batch size.
          _lowerCamelCase				:   List[str]								=   BatchSampler(range(2_1     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
          ]
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

          _lowerCamelCase				:   Tuple								=   BatchSampler(range(2_1     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   int								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
          ]
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
          # num_processes batch.
          _lowerCamelCase				:   Optional[Any]								=   BatchSampler(range(2_2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Optional[Any]								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
          ]
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

          _lowerCamelCase				:   Optional[int]								=   BatchSampler(range(2_2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Optional[int]								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
          ]
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
          # num_processes batch.
          _lowerCamelCase				:   Optional[Any]								=   BatchSampler(range(2_0     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   List[Any]								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
          ]
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

          _lowerCamelCase				:   List[str]								=   BatchSampler(range(2_0     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Union[str, Any]								=   [
              [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
              [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
          ]
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

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

          _lowerCamelCase				:   Optional[Any]								=   BatchSampler(range(2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   List[str]								=   [[], []]
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          # Check the shards when the dataset is a round multiple of batch size.
          _lowerCamelCase				:   Any								=   BatchSampler(range(2_4     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A,even_batches=__A     )

          _lowerCamelCase				:   Union[str, Any]								=   BatchSampler(range(2_4     ),batch_size=4,drop_last=__A     )
          # Expected shouldn't change
          self.check_batch_sampler_shards(__A,__A,split_batches=__A,even_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size.
          _lowerCamelCase				:   Optional[Any]								=   BatchSampler(range(2_2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A,even_batches=__A     )

          _lowerCamelCase				:   List[Any]								=   BatchSampler(range(2_2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A,even_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size or num_processes.
          _lowerCamelCase				:   str								=   BatchSampler(range(2_1     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Optional[Any]								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A,even_batches=__A     )

          _lowerCamelCase				:   Optional[int]								=   BatchSampler(range(2_1     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   str								=   [
              [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
              [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
          ]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A,even_batches=__A     )

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

          _lowerCamelCase				:   Optional[Any]								=   BatchSampler(range(2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Union[str, Any]								=   [[], []]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A,even_batches=__A     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   str								=   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
          _lowerCamelCase				:   List[str]								=   [BatchSamplerShard(__A,2,__A,even_batches=__A     ) 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], [1_2, 1_3]]     )
          self.assertListEqual(list(batch_sampler_shards[1]     ),[[3, 4], [9, 1_0, 1_1]]     )



    def   lowerCamelCase_  (	self      :       int,__A      :       List[str],__A      :       int,__A      :       List[str],__A      :       Optional[int]=False,__A      :       List[str]=2,__A      :       Optional[Any]=False     ):
          random.seed(__A     )
          _lowerCamelCase				:   str								=   list(__A     )

          _lowerCamelCase				:   List[str]								=   [
              IterableDatasetShard(
                  __A,batch_size=__A,drop_last=__A,num_processes=__A,process_index=__A,split_batches=__A,)
              for i in range(__A     )
          ]
          _lowerCamelCase				:   Dict								=   []
          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(__A     )
                iterable_dataset_lists.append(list(__A     )     )

          _lowerCamelCase				:   Union[str, Any]								=   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
          _lowerCamelCase				:   Any								=   iterable_dataset_lists[0]
          for l in iterable_dataset_lists[1:]:
                self.assertEqual(len(__A     ),len(__A     )     )
                self.assertTrue(len(__A     ) % shard_batch_size == 0     )

          _lowerCamelCase				:   Optional[int]								=   []
          for idx in range(0,len(__A     ),__A     ):
                for l in iterable_dataset_lists:
                      observed += l[idx : idx + shard_batch_size]

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



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   int								=   4_2
          _lowerCamelCase				:   Optional[int]								=   RandomIterableDataset()

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

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

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



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   List[Any]								=   BatchSampler(range(1_6     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   SkipBatchSampler(__A,2     )
          self.assertListEqual(list(__A     ),[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   str								=   SkipDataLoader(list(range(1_6     )     ),batch_size=4,skip_batches=2     )
          self.assertListEqual([t.tolist() for t in dataloader],[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   DataLoader(list(range(1_6     )     ),batch_size=4     )
          _lowerCamelCase				:   List[str]								=   skip_first_batches(__A,num_batches=2     )
          self.assertListEqual([t.tolist() for t in new_dataloader],[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]     )



    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   Optional[int]								=   DataLoaderShard(list(range(1_6     )     ),batch_size=4     )
          for idx, _ in enumerate(__A     ):
                self.assertEqual(dataloader.end_of_dataloader,idx == 3     )

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



    def   lowerCamelCase_  (	self      :       Dict     ):
          Accelerator()
          _lowerCamelCase				:   List[Any]								=   DataLoaderDispatcher(range(1_6     ),batch_size=4     )
          for idx, _ in enumerate(__A     ):
                self.assertEqual(dataloader.end_of_dataloader,idx == 3     )

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

'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




      if "img_encoder.pos_embed" in name:
            _lowerCamelCase				:   List[str]								=   name.replace("img_encoder.pos_embed"				,	"vision_model.embeddings.position_embeddings"							)
      if "img_encoder.patch_embed.proj" in name:
            _lowerCamelCase				:   Dict								=   name.replace("img_encoder.patch_embed.proj"				,	"vision_model.embeddings.patch_embeddings.projection"							)
      if "img_encoder.patch_embed.norm" in name:
            _lowerCamelCase				:   Optional[int]								=   name.replace("img_encoder.patch_embed.norm"				,	"vision_model.embeddings.layernorm"							)
      if "img_encoder.layers" in name:
            _lowerCamelCase				:   Optional[Any]								=   name.replace("img_encoder.layers"				,	"vision_model.encoder.stages"							)
      if "blocks" in name and "res" not in name:
            _lowerCamelCase				:   List[Any]								=   name.replace("blocks"				,	"layers"							)
      if "attn" in name and "pre_assign" not in name:
            _lowerCamelCase				:   str								=   name.replace("attn"				,	"self_attn"							)
      if "proj" in name and "self_attn" in name and "text" not in name:
            _lowerCamelCase				:   Tuple								=   name.replace("proj"				,	"out_proj"							)
      if "pre_assign_attn.attn.proj" in name:
            _lowerCamelCase				:   Union[str, Any]								=   name.replace("pre_assign_attn.attn.proj"				,	"pre_assign_attn.attn.out_proj"							)
      if "norm1" in name:
            _lowerCamelCase				:   Optional[Any]								=   name.replace("norm1"				,	"layer_norm1"							)
      if "norm2" in name and "pre_assign" not in name:
            _lowerCamelCase				:   Optional[int]								=   name.replace("norm2"				,	"layer_norm2"							)
      if "img_encoder.norm" in name:
            _lowerCamelCase				:   Dict								=   name.replace("img_encoder.norm"				,	"vision_model.layernorm"							)
      # text encoder
      if "text_encoder.token_embedding" in name:
            _lowerCamelCase				:   List[str]								=   name.replace("text_encoder.token_embedding"				,	"text_model.embeddings.token_embedding"							)
      if "text_encoder.positional_embedding" in name:
            _lowerCamelCase				:   Optional[Any]								=   name.replace("text_encoder.positional_embedding"				,	"text_model.embeddings.position_embedding.weight"							)
      if "text_encoder.transformer.resblocks." in name:
            _lowerCamelCase				:   Any								=   name.replace("text_encoder.transformer.resblocks."				,	"text_model.encoder.layers."							)
      if "ln_1" in name:
            _lowerCamelCase				:   str								=   name.replace("ln_1"				,	"layer_norm1"							)
      if "ln_2" in name:
            _lowerCamelCase				:   Optional[int]								=   name.replace("ln_2"				,	"layer_norm2"							)
      if "c_fc" in name:
            _lowerCamelCase				:   List[Any]								=   name.replace("c_fc"				,	"fc1"							)
      if "c_proj" in name:
            _lowerCamelCase				:   Tuple								=   name.replace("c_proj"				,	"fc2"							)
      if "text_encoder" in name:
            _lowerCamelCase				:   Tuple								=   name.replace("text_encoder"				,	"text_model"							)
      if "ln_final" in name:
            _lowerCamelCase				:   Optional[Any]								=   name.replace("ln_final"				,	"final_layer_norm"							)
      # projection layers
      if "img_projector.linear_hidden." in name:
            _lowerCamelCase				:   int								=   name.replace("img_projector.linear_hidden."				,	"visual_projection."							)
      if "img_projector.linear_out." in name:
            _lowerCamelCase				:   Tuple								=   name.replace("img_projector.linear_out."				,	"visual_projection.3."							)
      if "text_projector.linear_hidden" in name:
            _lowerCamelCase				:   Optional[Any]								=   name.replace("text_projector.linear_hidden"				,	"text_projection"							)
      if "text_projector.linear_out" in name:
            _lowerCamelCase				:   str								=   name.replace("text_projector.linear_out"				,	"text_projection.3"							)

      return name







def 		A_       (	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            _lowerCamelCase				:   Optional[int]								=   orig_state_dict.pop(_lowerCAmelCase							)

            if "qkv" in key:
                  # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                  # we need to split them up into separate matrices/vectors
                  _lowerCamelCase				:   Any								=   key.split("."							)
                  _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   int(key_split[2]							), int(key_split[4]							)
                  _lowerCamelCase				:   List[Any]								=   config.vision_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   int								=   val[dim : dim * 2, :]
                        _lowerCamelCase				:   str								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   Optional[int]								=   val[:dim]
                        _lowerCamelCase				:   str								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Tuple								=   val[-dim:]
            elif "in_proj" in key:
                  # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                  # we need to split them up into separate matrices/vectors
                  _lowerCamelCase				:   Optional[Any]								=   key.split("."							)
                  _lowerCamelCase				:   int								=   int(key_split[3]							)
                  _lowerCamelCase				:   Tuple								=   config.text_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   str								=   val[
                            dim : dim * 2, :
                        ]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   str								=   val[:dim]
                        _lowerCamelCase				:   Optional[Any]								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:]
            else:
                  _lowerCamelCase				:   int								=   rename_key(_lowerCAmelCase							)
                  # squeeze if necessary
                  if (
                      "text_projection.0" in new_name
                      or "text_projection.3" in new_name
                      or "visual_projection.0" in new_name
                      or "visual_projection.3" in new_name
                  ):
                        _lowerCamelCase				:   Any								=   val.squeeze_()
                  else:
                        _lowerCamelCase				:   Union[str, Any]								=   val

      return orig_state_dict







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   "http://images.cocodataset.org/val2017/000000039769.jpg"
      _lowerCamelCase				:   Any								=   Image.open(requests.get(_lowerCAmelCase				,	stream=_lowerCAmelCase							).raw							)
      return im







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    str="groupvit-gcc-yfcc"				,	_lowerCAmelCase   :    List[str]=False							):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   GroupViTConfig()
      _lowerCamelCase				:   Union[str, Any]								=   GroupViTModel(_lowerCAmelCase							).eval()

      _lowerCamelCase				:   Any								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]
      _lowerCamelCase				:   Union[str, Any]								=   convert_state_dict(_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   model.load_state_dict(_lowerCAmelCase				,	strict=_lowerCAmelCase							)
      assert missing_keys == ["text_model.embeddings.position_ids"]
      assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowerCAmelCase							) == 0)

      # verify result
      _lowerCamelCase				:   List[Any]								=   CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"							)
      _lowerCamelCase				:   List[str]								=   prepare_img()
      _lowerCamelCase				:   Any								=   processor(text=["a photo of a cat", "a photo of a dog"]				,	images=_lowerCAmelCase				,	padding=_lowerCAmelCase				,	return_tensors="pt"							)

      with torch.no_grad():
            _lowerCamelCase				:   List[str]								=   model(**_lowerCAmelCase							)

      if model_name == "groupvit-gcc-yfcc":
            _lowerCamelCase				:   List[Any]								=   torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]							)
      elif model_name == "groupvit-gcc-redcaps":
            _lowerCamelCase				:   Optional[Any]								=   torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]							)
      else:
            raise ValueError(F'Model name {model_name} not supported.'							)
      assert torch.allclose(outputs.logits_per_image				,	_lowerCAmelCase				,	atol=1E-3							)

      processor.save_pretrained(_lowerCAmelCase							)
      model.save_pretrained(_lowerCAmelCase							)
      print("Successfully saved processor and model to"				,	_lowerCAmelCase							)

      if push_to_hub:
            print("Pushing to the hub..."							)
            processor.push_to_hub(_lowerCAmelCase				,	organization="nielsr"							)
            model.push_to_hub(_lowerCAmelCase				,	organization="nielsr"							)


if __name__ == "__main__":
 UpperCAmelCase_       :   Optional[int]   =       argparse.ArgumentParser()
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
 )
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
 parser.add_argument(
     '--model_name',
     default='groupvit-gccy-fcc',
     type=str,
     help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
     help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
 )
 UpperCAmelCase_       :   Union[str, Any]   =       parser.parse_args()

 convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)